



import pickle

import numpy as np
from matplotlib import pyplot as plt






class 						lowercase		:






	"""simple docstring"""


	def __init__(      self      :		Optional[Any]		,    __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		List[Any]		,    __UpperCAmelCase      :		Optional[Any]		,    __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		Tuple=0.2		,    __UpperCAmelCase      :		Optional[Any]=0.2	)     ->					Any:
							UpperCAmelCase_=  bp_numa
							UpperCAmelCase_=  bp_numa
							UpperCAmelCase_=  bp_numa
							UpperCAmelCase_=  conva_get[:2]
							UpperCAmelCase_=  conva_get[2]
							UpperCAmelCase_=  size_pa
							UpperCAmelCase_=  rate_w
							UpperCAmelCase_=  rate_t
							UpperCAmelCase_=  [
							    np.mat(-1 * np.random.rand(self.conva[0]		,    self.conva[0]	) + 0.5	)
							    for i in range(self.conva[1]	)
							]
							UpperCAmelCase_=  np.mat(-1 * np.random.rand(self.num_bpa		,    self.num_bpa	) + 0.5	)
							UpperCAmelCase_=  np.mat(-1 * np.random.rand(self.num_bpa		,    self.num_bpa	) + 0.5	)
							UpperCAmelCase_=  -2 * np.random.rand(self.conva[1]	) + 1
							UpperCAmelCase_=  -2 * np.random.rand(self.num_bpa	) + 1
							UpperCAmelCase_=  -2 * np.random.rand(self.num_bpa	) + 1




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[int]		,    __UpperCAmelCase      :		Dict	)     ->					str:
							# save model dict with pickle
							UpperCAmelCase_=  {
							    """num_bp1""": self.num_bpa,
							    """num_bp2""": self.num_bpa,
							    """num_bp3""": self.num_bpa,
							    """conv1""": self.conva,
							    """step_conv1""": self.step_conva,
							    """size_pooling1""": self.size_poolinga,
							    """rate_weight""": self.rate_weight,
							    """rate_thre""": self.rate_thre,
							    """w_conv1""": self.w_conva,
							    """wkj""": self.wkj,
							    """vji""": self.vji,
							    """thre_conv1""": self.thre_conva,
							    """thre_bp2""": self.thre_bpa,
							    """thre_bp3""": self.thre_bpa,
							}
							with open(_lowerCamelCase		,    """wb"""	) as f:
													pickle.dump(_lowerCamelCase		,    _lowerCamelCase	)

							print(F"""Model saved： {save_path}"""	)




	@classmethod
	def     _SCREAMING_SNAKE_CASE						(      cls      :		Union[str, Any]		,    __UpperCAmelCase      :		List[str]	)     ->					str:
							# read saved model
							with open(_lowerCamelCase		,    """rb"""	) as f:
													UpperCAmelCase_=  pickle.load(_lowerCamelCase	)  # noqa: S301

							UpperCAmelCase_=  model_dic.get("""conv1"""	)
							conv_get.append(model_dic.get("""step_conv1"""	)	)
							UpperCAmelCase_=  model_dic.get("""size_pooling1"""	)
							UpperCAmelCase_=  model_dic.get("""num_bp1"""	)
							UpperCAmelCase_=  model_dic.get("""num_bp2"""	)
							UpperCAmelCase_=  model_dic.get("""num_bp3"""	)
							UpperCAmelCase_=  model_dic.get("""rate_weight"""	)
							UpperCAmelCase_=  model_dic.get("""rate_thre"""	)
							# create model instance
							UpperCAmelCase_=  CNN(_lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase		,    _lowerCamelCase	)
							# modify model parameter
							UpperCAmelCase_=  model_dic.get("""w_conv1"""	)
							UpperCAmelCase_=  model_dic.get("""wkj"""	)
							UpperCAmelCase_=  model_dic.get("""vji"""	)
							UpperCAmelCase_=  model_dic.get("""thre_conv1"""	)
							UpperCAmelCase_=  model_dic.get("""thre_bp2"""	)
							UpperCAmelCase_=  model_dic.get("""thre_bp3"""	)
							return conv_ins




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[str]		,    __UpperCAmelCase      :		str	)     ->					Optional[Any]:
							return 1 / (1 + np.exp(-1 * x	))




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[int]		,    __UpperCAmelCase      :		str	)     ->					Union[str, Any]:
							return round(_lowerCamelCase		,    3	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[int]		,    __UpperCAmelCase      :		Union[str, Any]		,    __UpperCAmelCase      :		Union[str, Any]		,    __UpperCAmelCase      :		Optional[Any]		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		List[str]	)     ->					Union[str, Any]:
							# convolution process
							UpperCAmelCase_=  convs[0]
							UpperCAmelCase_=  convs[1]
							UpperCAmelCase_=  np.shape(_lowerCamelCase	)[0]
							# get the data slice of original image data, data_focus
							UpperCAmelCase_=  []
							for i_focus in range(0		,    size_data - size_conv + 1		,    _lowerCamelCase	):
													for j_focus in range(0		,    size_data - size_conv + 1		,    _lowerCamelCase	):
																			UpperCAmelCase_=  data[
																			    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
																			]
																			data_focus.append(_lowerCamelCase	)
        # calculate the feature map of every single kernel, and saved as list of matrix
							UpperCAmelCase_=  []
							UpperCAmelCase_=  int((size_data - size_conv) / conv_step + 1	)
							for i_map in range(_lowerCamelCase	):
													UpperCAmelCase_=  []
													for i_focus in range(len(_lowerCamelCase	)	):
																			UpperCAmelCase_=  (
																			    np.sum(np.multiply(data_focus[i_focus]		,    w_convs[i_map]	)	)
																			    - thre_convs[i_map]
																			)
																			featuremap.append(self.sig(_lowerCamelCase	)	)
													UpperCAmelCase_=  np.asmatrix(_lowerCamelCase	).reshape(
													    _lowerCamelCase		,    _lowerCamelCase	)
													data_featuremap.append(_lowerCamelCase	)

							# expanding the data slice to One dimenssion
							UpperCAmelCase_=  []
							for each_focus in data_focus:
													focusa_list.extend(self.Expand_Mat(_lowerCamelCase	)	)
							UpperCAmelCase_=  np.asarray(_lowerCamelCase	)
							return focus_list, data_featuremap




	def     _SCREAMING_SNAKE_CASE						(      self      :		Tuple		,    __UpperCAmelCase      :		str		,    __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		str="average_pool"	)     ->					Any:
							# pooling process
							UpperCAmelCase_=  len(featuremaps[0]	)
							UpperCAmelCase_=  int(size_map / size_pooling	)
							UpperCAmelCase_=  []
							for i_map in range(len(_lowerCamelCase	)	):
													UpperCAmelCase_=  featuremaps[i_map]
													UpperCAmelCase_=  []
													for i_focus in range(0		,    _lowerCamelCase		,    _lowerCamelCase	):
																			for j_focus in range(0		,    _lowerCamelCase		,    _lowerCamelCase	):
																									UpperCAmelCase_=  feature_map[
																									    i_focus : i_focus + size_pooling,
																									    j_focus : j_focus + size_pooling,
																									]
																									if pooling_type == "average_pool":
																															# average pooling
																															map_pooled.append(np.average(_lowerCamelCase	)	)
																									elif pooling_type == "max_pooling":
																															# max pooling
																															map_pooled.append(np.max(_lowerCamelCase	)	)
													UpperCAmelCase_=  np.asmatrix(_lowerCamelCase	).reshape(_lowerCamelCase		,    _lowerCamelCase	)
													featuremap_pooled.append(_lowerCamelCase	)
							return featuremap_pooled




	def     _SCREAMING_SNAKE_CASE						(      self      :		Any		,    __UpperCAmelCase      :		Dict	)     ->					List[Any]:
							# expanding three dimension data to one dimension list
							UpperCAmelCase_=  []
							for i in range(len(_lowerCamelCase	)	):
													UpperCAmelCase_=  np.shape(data[i]	)
													UpperCAmelCase_=  data[i].reshape(1		,    shapes[0] * shapes[1]	)
													UpperCAmelCase_=  data_listed.getA().tolist()[0]
													data_expanded.extend(_lowerCamelCase	)
							UpperCAmelCase_=  np.asarray(_lowerCamelCase	)
							return data_expanded




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[Any]		,    __UpperCAmelCase      :		List[str]	)     ->					Optional[Any]:
							# expanding matrix to one dimension list
							UpperCAmelCase_=  np.asarray(_lowerCamelCase	)
							UpperCAmelCase_=  np.shape(_lowerCamelCase	)
							UpperCAmelCase_=  data_mat.reshape(1		,    shapes[0] * shapes[1]	)
							return data_expanded




	def     _SCREAMING_SNAKE_CASE						(      self      :		Tuple		,    __UpperCAmelCase      :		List[str]		,    __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		Dict		,    __UpperCAmelCase      :		Any		,    __UpperCAmelCase      :		Union[str, Any]	)     ->					Optional[Any]:
							UpperCAmelCase_=  []
							UpperCAmelCase_=  0
							for i_map in range(_lowerCamelCase	):
													UpperCAmelCase_=  np.ones((size_map, size_map)	)
													for i in range(0		,    _lowerCamelCase		,    _lowerCamelCase	):
																			for j in range(0		,    _lowerCamelCase		,    _lowerCamelCase	):
																									UpperCAmelCase_=  pd_pool[
																									    i_pool
																									]
																									UpperCAmelCase_=  i_pool + 1
													UpperCAmelCase_=  np.multiply(
													    _lowerCamelCase		,    np.multiply(out_map[i_map]		,    (1 - out_map[i_map])	)	)
													pd_all.append(_lowerCamelCase	)
							return pd_all




	def     _SCREAMING_SNAKE_CASE						(      self      :		List[str]		,    __UpperCAmelCase      :		Optional[Any]		,    __UpperCAmelCase      :		Union[str, Any]		,    __UpperCAmelCase      :		Optional[Any]		,    __UpperCAmelCase      :		List[Any]		,    __UpperCAmelCase      :		Optional[int]		,    __UpperCAmelCase      :		Union[str, Any]=bool	)     ->					List[str]:
							# model traning
							print("""----------------------Start Training-------------------------"""	)
							print((""" - - Shape: Train_Data  """, np.shape(_lowerCamelCase	))	)
							print((""" - - Shape: Teach_Data  """, np.shape(_lowerCamelCase	))	)
							UpperCAmelCase_=  0
							UpperCAmelCase_=  []
							UpperCAmelCase_=  10_000
							while rp < n_repeat and mse >= error_accuracy:
													UpperCAmelCase_=  0
													print(F"""-------------Learning Time {rp}--------------"""	)
													for p in range(len(_lowerCamelCase	)	):
																			# print('------------Learning Image: %d--------------'%p)
																			UpperCAmelCase_=  np.asmatrix(datas_train[p]	)
																			UpperCAmelCase_=  np.asarray(datas_teach[p]	)
																			UpperCAmelCase_,  UpperCAmelCase_=  self.convolute(
																			    _lowerCamelCase		,    self.conva		,    self.w_conva		,    self.thre_conva		,    conv_step=self.step_conva		,    )
																			UpperCAmelCase_=  self.pooling(_lowerCamelCase		,    self.size_poolinga	)
																			UpperCAmelCase_=  np.shape(_lowerCamelCase	)
																			UpperCAmelCase_=  self._expand(_lowerCamelCase	)
																			UpperCAmelCase_=  data_bp_input

																			UpperCAmelCase_=  np.dot(_lowerCamelCase		,    self.vji.T	) - self.thre_bpa
																			UpperCAmelCase_=  self.sig(_lowerCamelCase	)
																			UpperCAmelCase_=  np.dot(_lowerCamelCase		,    self.wkj.T	) - self.thre_bpa
																			UpperCAmelCase_=  self.sig(_lowerCamelCase	)

																			# --------------Model Leaning ------------------------
																			# calculate error and gradient---------------
																			UpperCAmelCase_=  np.multiply(
																			    (data_teach - bp_outa)		,    np.multiply(_lowerCamelCase		,    (1 - bp_outa)	)	)
																			UpperCAmelCase_=  np.multiply(
																			    np.dot(_lowerCamelCase		,    self.wkj	)		,    np.multiply(_lowerCamelCase		,    (1 - bp_outa)	)	)
																			UpperCAmelCase_=  np.dot(_lowerCamelCase		,    self.vji	)

																			UpperCAmelCase_=  pd_i_all / (self.size_poolinga * self.size_poolinga)
																			UpperCAmelCase_=  pd_conva_pooled.T.getA().tolist()
																			UpperCAmelCase_=  self._calculate_gradient_from_pool(
																			    _lowerCamelCase		,    _lowerCamelCase		,    shape_featuremapa[0]		,    shape_featuremapa[1]		,    self.size_poolinga		,    )
																			# weight and threshold learning process---------
																			# convolution layer
																			for k_conv in range(self.conva[1]	):
																									UpperCAmelCase_=  self._expand_mat(pd_conva_all[k_conv]	)
																									UpperCAmelCase_=  self.rate_weight * np.dot(_lowerCamelCase		,    _lowerCamelCase	)

																									UpperCAmelCase_=  self.w_conva[k_conv] + delta_w.reshape(
																									    (self.conva[0], self.conva[0])	)

																									UpperCAmelCase_=  (
																									    self.thre_conva[k_conv]
																									    - np.sum(pd_conva_all[k_conv]	) * self.rate_thre
																									)
																			# all connected layer
																			UpperCAmelCase_=  self.wkj + pd_k_all.T * bp_outa * self.rate_weight
																			UpperCAmelCase_=  self.vji + pd_j_all.T * bp_outa * self.rate_weight
																			UpperCAmelCase_=  self.thre_bpa - pd_k_all * self.rate_thre
																			UpperCAmelCase_=  self.thre_bpa - pd_j_all * self.rate_thre
																			# calculate the sum error of all single image
																			UpperCAmelCase_=  np.sum(abs(data_teach - bp_outa	)	)
																			error_count += errors
																			# print('   ----Teach      ',data_teach)
																			# print('   ----BP_output  ',bp_out3)
													UpperCAmelCase_=  rp + 1
													UpperCAmelCase_=  error_count / patterns
													all_mse.append(_lowerCamelCase	)

							def draw_error():
													UpperCAmelCase_=  [error_accuracy for i in range(int(n_repeat * 1.2	)	)]
													plt.plot(_lowerCamelCase		,    """+-"""	)
													plt.plot(_lowerCamelCase		,    """r--"""	)
													plt.xlabel("""Learning Times"""	)
													plt.ylabel("""All_mse"""	)
													plt.grid(_lowerCamelCase		,    alpha=0.5	)
													plt.show()

							print("""------------------Training Complished---------------------"""	)
							print((""" - - Training epoch: """, rp, F"""     - - Mse: {mse:.6f}""")	)
							if draw_e:
													draw_error()
							return mse




	def     _SCREAMING_SNAKE_CASE						(      self      :		Any		,    __UpperCAmelCase      :		List[Any]	)     ->					Tuple:
							# model predict
							UpperCAmelCase_=  []
							print("""-------------------Start Testing-------------------------"""	)
							print((""" - - Shape: Test_Data  """, np.shape(_lowerCamelCase	))	)
							for p in range(len(_lowerCamelCase	)	):
													UpperCAmelCase_=  np.asmatrix(datas_test[p]	)
													UpperCAmelCase_,  UpperCAmelCase_=  self.convolute(
													    _lowerCamelCase		,    self.conva		,    self.w_conva		,    self.thre_conva		,    conv_step=self.step_conva		,    )
													UpperCAmelCase_=  self.pooling(_lowerCamelCase		,    self.size_poolinga	)
													UpperCAmelCase_=  self._expand(_lowerCamelCase	)

													UpperCAmelCase_=  data_bp_input
													UpperCAmelCase_=  bp_outa * self.vji.T - self.thre_bpa
													UpperCAmelCase_=  self.sig(_lowerCamelCase	)
													UpperCAmelCase_=  bp_outa * self.wkj.T - self.thre_bpa
													UpperCAmelCase_=  self.sig(_lowerCamelCase	)
													produce_out.extend(bp_outa.getA().tolist()	)
							UpperCAmelCase_=  [list(map(self.do_round		,    _lowerCamelCase	)	) for each in produce_out]
							return np.asarray(_lowerCamelCase	)




	def     _SCREAMING_SNAKE_CASE						(      self      :		Optional[int]		,    __UpperCAmelCase      :		Optional[Any]	)     ->					Any:
							# return the data of image after convoluting process so we can check it out
							UpperCAmelCase_=  np.asmatrix(_lowerCamelCase	)
							UpperCAmelCase_,  UpperCAmelCase_=  self.convolute(
							    _lowerCamelCase		,    self.conva		,    self.w_conva		,    self.thre_conva		,    conv_step=self.step_conva		,    )
							UpperCAmelCase_=  self.pooling(_lowerCamelCase		,    self.size_poolinga	)

							return data_conveda, data_pooleda


if __name__ == "__main__":
	pass





"""simple docstring"""


def      lowerCamelCase_					(   __lowerCAmelCase							)				->   bool:




						'''simple docstring'''



						return number & 1 == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()


